1st INCF Workshop on NeuroImaging Database Integration

The goal of this meeting was to map existing neuroimaging databases, particularly databases containing primary data, and to identify mechanisms that could facilitate integrated use of such databases, including possible fusion of databases. The report provides an overview of existing neuroimaging databases that were discussed during the workshop and examines the feasibility of database federations. The report includes several recommendations for future developments

Mandating Vaccination

A short piece exploring some arguments for mandating vaccination for Covid-19

Assurance Benefits of ISO 26262 compliant Microcontrollers for safety-critical Avionics

The usage of complex Microcontroller Units (MCUs) in avionic systems constitutes a challenge in assuring their safety. They are not developed according to the development requirements accepted by the aerospace industry. These Commercial off-the-shelf (COTS) hardware components usually target other domains like the telecommunication branch. In the last years MCUs developed in compliance to the ISO 26262 have been released on the market for safety-related automotive applications. The avionic assurance process could profit from these safety MCUs. In this paper we present evaluation results based on the current assurance practice that demonstrates expected assurance activities benefit from ISO 26262 compliant MCUs.Comment: Submitted to SafeComp 2018: http://www.es.mdh.se/safecomp2018

The Mean Variance Mixing GARCH (1,1) model

Here we present a general framework for a GARCH (1,1) type of process with innovations with a probability law of the mean- variance mixing type, therefore we call the process in question the mean variance mixing GARCH \ (1,1) or MVM GARCH\(1,1). One implication is a GARCH\ model with skewed innovations and constant mean dynamics. This is achieved without using a location parameter to compensate for time dependence that affects the mean dynamics. From a probabilistic viewpoint the idea is straightforward. We just construct our stochastic process from the desired behavior of the cumulants. Further we provide explicit expressions for the unconditional second to fourth cumulants for the process in question. In the paper we present a specification of the MVM-GARCH process where the mixing variable is of the inverse Gaussian type. On the basis on this assumption we can formulate a maximum likelihood based approach for estimating the process closely related to the approach used to estimate an ordinary GARCH (1,1). Under the distributional assumption that the mixing random process is an inverse Gaussian i.i.d process the MVM-GARCH process is then estimated on log return data from the Standard and Poor 500 index. An analysis for the conditional skewness and kurtosis implied by the process is also presented in the paperGARCH Skewness Conditional Skewness